Passive and Active Acoustic Sensing for Soft Pneumatic Actuators

TL;DR

This paper introduces an acoustic sensing method for soft pneumatic actuators using embedded microphones and speakers, enabling accurate, reliable, and robust state measurement through machine learning analysis of sound signals.

Contribution

It presents a novel sensorization approach that leverages sound modulation and machine learning to infer multiple actuator states, enhancing soft robot sensing capabilities.

Findings

Achieved 93% accuracy in contact location classification

Mean spatial accuracy of 3.7 mm in sensing tasks

Robust performance with 20 ms white noise and SVM classifier

Abstract

We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding state from sound recordings. We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and use it to measure contact locations, contact forces, object materials, actuator inflation, and actuator temperature. We show that the sensor is reliable (average classification rate for six contact locations of 93%), precise (mean spatial accuracy of 3.7 mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20 ms of white noise and a support vector classifier as the sensor model.